






"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402







class      UpperCamelCase_			(unittest.TestCase	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     List[str]:
				UpperCAmelCase_	:					Optional[Any]     	=   0





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Optional[int]:
				UpperCAmelCase_	:					Optional[int]     	=   AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"		)
				self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Any:
				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_	:					Tuple     	=   Path(lowerCAmelCase_		) / "preprocessor_config.json"
							UpperCAmelCase_	:					Optional[Any]     	=   Path(lowerCAmelCase_		) / "config.json"
							json.dump(
							    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}      ,      open(lowerCAmelCase_      ,      "w"		)      ,      )
							json.dump({"model_type": "clip"}      ,      open(lowerCAmelCase_      ,      "w"		)		)

							UpperCAmelCase_	:					Optional[Any]     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		)
							self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     List[Any]:
				# Ensure we can load the image processor from the feature extractor config
				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_	:					List[Any]     	=   Path(lowerCAmelCase_		) / "preprocessor_config.json"
							UpperCAmelCase_	:					Dict     	=   Path(lowerCAmelCase_		) / "config.json"
							json.dump(
							    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}      ,      open(lowerCAmelCase_      ,      "w"		)      ,      )
							json.dump({"model_type": "clip"}      ,      open(lowerCAmelCase_      ,      "w"		)		)

							UpperCAmelCase_	:					List[Any]     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		)
							self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Union[str, Any]:
				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_	:					str     	=   CLIPConfig()

							# Create a dummy config file with image_proceesor_type
							UpperCAmelCase_	:					Union[str, Any]     	=   Path(lowerCAmelCase_		) / "preprocessor_config.json"
							UpperCAmelCase_	:					List[Any]     	=   Path(lowerCAmelCase_		) / "config.json"
							json.dump(
							    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}      ,      open(lowerCAmelCase_      ,      "w"		)      ,      )
							json.dump({"model_type": "clip"}      ,      open(lowerCAmelCase_      ,      "w"		)		)

							# remove image_processor_type to make sure config.json alone is enough to load image processor locally
							UpperCAmelCase_	:					str     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		).to_dict()

							config_dict.pop("image_processor_type"		)
							UpperCAmelCase_	:					Dict     	=   CLIPImageProcessor(**lowerCAmelCase_		)

							# save in new folder
							model_config.save_pretrained(lowerCAmelCase_		)
							config.save_pretrained(lowerCAmelCase_		)

							UpperCAmelCase_	:					List[Any]     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		)

							# make sure private variable is not incorrectly saved
							UpperCAmelCase_	:					List[Any]     	=   json.loads(config.to_json_string()		)
							self.assertTrue("_processor_class" not in dict_as_saved		)

				self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Optional[int]:
				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_	:					Optional[Any]     	=   Path(lowerCAmelCase_		) / "preprocessor_config.json"
							json.dump(
							    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}      ,      open(lowerCAmelCase_      ,      "w"		)      ,      )

							UpperCAmelCase_	:					Optional[int]     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		)
							self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     str:
				with self.assertRaisesRegex(
				    lowerCAmelCase_      ,      "clip-base is not a local folder and is not a valid model identifier"		):
							UpperCAmelCase_	:					Any     	=   AutoImageProcessor.from_pretrained("clip-base"		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Union[str, Any]:
				with self.assertRaisesRegex(
				    lowerCAmelCase_      ,      R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"		):
							UpperCAmelCase_	:					Optional[Any]     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_      ,      revision="aaaaaa"		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Dict:
				with self.assertRaisesRegex(
				    lowerCAmelCase_      ,      "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."      ,      ):
							UpperCAmelCase_	:					List[Any]     	=   AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model"		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Union[str, Any]:
				# If remote code is not set, we will time out when asking whether to load the model.
				with self.assertRaises(lowerCAmelCase_		):
							UpperCAmelCase_	:					List[str]     	=   AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"		)
				# If remote code is disabled, we can't load this config.
				with self.assertRaises(lowerCAmelCase_		):
							UpperCAmelCase_	:					Dict     	=   AutoImageProcessor.from_pretrained(
							    "hf-internal-testing/test_dynamic_image_processor"      ,      trust_remote_code=lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   AutoImageProcessor.from_pretrained(
				    "hf-internal-testing/test_dynamic_image_processor"      ,      trust_remote_code=lowerCAmelCase_		)
				self.assertEqual(image_processor.__class__.__name__      ,      "NewImageProcessor"		)

				# Test image processor can be reloaded.
				with tempfile.TemporaryDirectory() as tmp_dir:
							image_processor.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_	:					Union[str, Any]     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_      ,      trust_remote_code=lowerCAmelCase_		)
				self.assertEqual(reloaded_image_processor.__class__.__name__      ,      "NewImageProcessor"		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Union[str, Any]:
				try:
							AutoConfig.register("custom"      ,      lowerCAmelCase_		)
							AutoImageProcessor.register(lowerCAmelCase_      ,      lowerCAmelCase_		)
							# Trying to register something existing in the Transformers library will raise an error
							with self.assertRaises(lowerCAmelCase_		):
										AutoImageProcessor.register(lowerCAmelCase_      ,      lowerCAmelCase_		)

							with tempfile.TemporaryDirectory() as tmpdirname:
										UpperCAmelCase_	:					List[str]     	=   Path(lowerCAmelCase_		) / "preprocessor_config.json"
										UpperCAmelCase_	:					Optional[Any]     	=   Path(lowerCAmelCase_		) / "config.json"
										json.dump(
										    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}      ,      open(lowerCAmelCase_      ,      "w"		)      ,      )
										json.dump({"model_type": "clip"}      ,      open(lowerCAmelCase_      ,      "w"		)		)

										UpperCAmelCase_	:					Tuple     	=   CustomImageProcessor.from_pretrained(lowerCAmelCase_		)

							# Now that the config is registered, it can be used as any other config with the auto-API
							with tempfile.TemporaryDirectory() as tmp_dir:
										image_processor.save_pretrained(lowerCAmelCase_		)
										UpperCAmelCase_	:					Any     	=   AutoImageProcessor.from_pretrained(lowerCAmelCase_		)
										self.assertIsInstance(lowerCAmelCase_      ,      lowerCAmelCase_		)

				finally:
							if "custom" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
										del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[str]:







				class      UpperCamelCase_			(__A	):
					__magic_name__       =							True

				try:
							AutoConfig.register("custom"      ,      lowerCAmelCase_		)
							AutoImageProcessor.register(lowerCAmelCase_      ,      lowerCAmelCase_		)
							# If remote code is not set, the default is to use local
							UpperCAmelCase_	:					Tuple     	=   AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"		)
							self.assertEqual(image_processor.__class__.__name__      ,      "NewImageProcessor"		)
							self.assertTrue(image_processor.is_local		)

							# If remote code is disabled, we load the local one.
							UpperCAmelCase_	:					Optional[int]     	=   AutoImageProcessor.from_pretrained(
							    "hf-internal-testing/test_dynamic_image_processor"      ,      trust_remote_code=lowerCAmelCase_		)
							self.assertEqual(image_processor.__class__.__name__      ,      "NewImageProcessor"		)
							self.assertTrue(image_processor.is_local		)

							# If remote is enabled, we load from the Hub
							UpperCAmelCase_	:					str     	=   AutoImageProcessor.from_pretrained(
							    "hf-internal-testing/test_dynamic_image_processor"      ,      trust_remote_code=lowerCAmelCase_		)
							self.assertEqual(image_processor.__class__.__name__      ,      "NewImageProcessor"		)
							self.assertTrue(not hasattr(lowerCAmelCase_      ,      "is_local"		)		)

				finally:
							if "custom" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
										del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowerCamelCase__  =					get_logger(__name__)




class 					_UpperCAmelCase			(     enum.Enum  ):



 '''simple docstring'''




 __A			       =					'''all_checks'''
 __A			       =					'''basic_checks'''
 __A			       =					'''no_checks'''




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''






def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__=None )      ->int:




  '''simple docstring'''

  if expected_checksums is None:
    logger.info("Unable to verify checksums." )
    return
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(a__ ) - set(a__ ) ) )
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise UnexpectedDownloadedFile(str(set(a__ ) - set(a__ ) ) )
  _UpperCamelCase           =    [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  _UpperCamelCase           =    " for " + verification_name if verification_name is not None else ""
  if len(a__ ) > 0:
    raise NonMatchingChecksumError(
        f'Checksums didn\'t match{for_verification_name}:\n'
        f'{bad_urls}\n'
        "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
  logger.info("All the checksums matched successfully" + for_verification_name )




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''








class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''






def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Dict:




  '''simple docstring'''

  if expected_splits is None:
    logger.info("Unable to verify splits sizes." )
    return
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise ExpectedMoreSplits(str(set(a__ ) - set(a__ ) ) )
  if len(set(a__ ) - set(a__ ) ) > 0:
    raise UnexpectedSplits(str(set(a__ ) - set(a__ ) ) )
  _UpperCamelCase           =    [
      {"expected": expected_splits[name], "recorded": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(a__ ) > 0:
    raise NonMatchingSplitsSizesError(str(a__ ) )
  logger.info("All the splits matched successfully." )


def 				lowerCAmelCase__       (			a__    ,     a__ = True )      ->dict:




  '''simple docstring'''

  if record_checksum:
    _UpperCamelCase           =    shaaaa()
    with open(a__    ,     "rb" ) as f:
      for chunk in iter(lambda: f.read(1 << 20 )    ,     B"" ):
        m.update(a__ )
      _UpperCamelCase           =    m.hexdigest()
  else:
    _UpperCamelCase           =    None
  return {"num_bytes": os.path.getsize(a__ ), "checksum": checksum}







def 				lowerCAmelCase__       (			a__ )      ->Any:




  '''simple docstring'''

  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False




